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Unsupervised feature selection for balanced clustering
Knowledge-Based Systems, 2020Abstract In many real-world applications of data mining, such as energy load balance of wireless sensor networks, given data points with balanced distribution, i.e., each class contains approximately the same number of instances, we often need to obtain a clustering result to reflect such balance.
Peng Zhou 0006 +5 more
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Unsupervised feature selection with ordinal locality
2017 IEEE International Conference on Multimedia and Expo (ICME), 2017Unsupervised feature selection has shown significant potential in distance-based clustering tasks. This paper proposes a novel triplet induced method. Firstly, a triplet-based loss function is introduced to enforce the selected feature groups to preserve ordinal locality of original data, which contributes to distance-based clustering tasks.
Jun Guo 0008 +3 more
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Unsupervised Feature Selection for Ensemble of Classifiers
Ninth International Workshop on Frontiers in Handwriting Recognition, 2004In this paper we discuss a strategy to create ensemble of classifiers based on unsupervised features selection. It takes into account a hierarchical multi-objective genetic algorithm that generates a set of classifiers by performing feature selection and then combines them to provide a set of powerful ensembles.
Marisa E. Morita +2 more
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Discriminative embedded unsupervised feature selection
Pattern Recognition Letters, 2018Abstract Unsupervised feature selection is a powerful tool to process high-dimensional data, in which a subset of features are selected out for effective data representation. In this paper, we propose a novel unsupervised feature selection method which discovers and exploits the global information of the data by maximizing distances between samples ...
Qi-Hai Zhu, Yu-Bin Yang
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Co-regularized unsupervised feature selection
Neurocomputing, 2018Abstract Unsupervised feature selection (UFS) is very challenging due to the lack of label information. Most UFS methods generate pseudo labels by spectral clustering, matrix factorization or dictionary learning, and convert UFS into a supervised feature selection problem. Generally, the features that can preserve the data distribution (i.e., cluster
Pengfei Zhu 0001 +3 more
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Feature Selection with Unsupervised Consensus Guidance
IEEE Transactions on Knowledge and Data Engineering, 2019Most of the unsupervised feature selection methods employ pseudo labels generated by clustering to guide the feature selection; however, noisy and irrelevant features degrade the cluster structure, which is ineffective to supervise feature selection.
Hongfu Liu 0001, Ming Shao, Yun Fu 0001
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Unsupervised Joint Feature Discretization and Selection
2011In many applications, we deal with high dimensional datasets with different types of data. For instance, in text classification and information retrieval problems, we have large collections of documents. Each text is usually represented by a bag-of-words or similar representation, with a large number of features (terms).
Artur J. Ferreira +1 more
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An efficient framework for unsupervised feature selection
Neurocomputing, 2019Abstract In these years, the task of fast unsupervised feature selection attracts much attentions with the increasing number of data collected from the physical world. To speed up the running time of algorithms, the bipartite graph theory has been applied in many large-scale tasks, including fast clustering, fast feature extraction, etc.
Han Zhang 0012 +3 more
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Exploring autoencoders for unsupervised feature selection
2015 International Joint Conference on Neural Networks (IJCNN), 2015Feature selection plays an important role in pattern classification. It is especially an important preprocessing task when there are large number of features in comparison to number of patterns as is the case with gene expression data. A new unsupervised feature selection method has been evolved using autoencoders since autoencoders have the capacity ...
B. Chandra 0001 +1 more
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Unsupervised Feature Selection by Graph Optimization
2015Graph based methods have played an important role in machine learning due to their ability to encode the similarity relationships among data. A commonly used criterion in graph based feature selection methods is to select the features which best preserve the data similarity or a manifold structure derived from the entire feature set.
Zhihong Zhang 0001 +3 more
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